Course ObjectiveIn this course the student will learn how to find unbiased gradient
estimators and how to apply them in stochastic simulation-based
optimization and learning algorithms. After successful participation in
this course the student will be able to conduct a gradient-based
stochastic optimization solution to real life problems.
Course ContentIn presence of uncertainty, gradients typically fail to be available in
analytical form and optimization has to resort to simulation-based
algorithms. Unbiased gradient estimators are a main ingredient in
simulation-based optimization methods. The focus of this course is on
unbiased gradient estimators and their application in stochastic
simulation-based optimization and learning algorithms. Next to classical
stochastic gradient methods, this course also covers a range of related
topics such as model and parameter insecurity, robust optimization and
sample average approximation. Applications will stem from a wide range
of domains from Financial Engineering over Inventory Management to
Waiting Time Minimization and neural networks.
This is course on advanced simulation techniques. The methodological
part of the course focusses on the theory of recursive learning and
optimization algorithms known as stochastic approximation.
Teaching MethodsCombined lectures and tutorials
Method of AssessmentFinal exam – Individual assessment
Individual assignment - Individual assessment
LiteratureHandout of monograph “Gradient based Stochastic Optimization”, B.
Heidergott and F. Vásquez-Abad, 2018.
Additional InformationThe course is suitable to be taken in an exchange program.
Recommended background knowledgeAnalysis, basic probability theory, basic programming
|Language of Tuition||English|
|Faculty||School of Business and Economics|
|Course Coordinator||prof. dr. B.F. Heidergott|
|Examiner||prof. dr. B.F. Heidergott|
dr. A.A.N. Ridder
prof. dr. B.F. Heidergott
You need to register for this course yourself
Last-minute registration is available for this course.
|Teaching Methods||Lecture, Study Group|
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